The value of synthetic high-resolution daily snow cover maps for long-term hydrological modeling

To study long-term changes in the hydrology of snow-fed catchments, there is a need for long-term time series of snow cover data. Satellite imagery and climate reanalysis can both be used to quantify past snow cover, but they lack in baseline period length and spatial resolution respectively. In this study we apply statistical methods to generate synthetic high-resolution daily snow cover maps, and consequently use these maps as forcing in long-term hydrological modeling. The results are benchmarked against a case without synthetic snow cover forcing. Multiple hydrological models are used to reduce the uncertainty related to the model choice. The study is performed on the Thur catchment in Eastern Switzerland, a meso-scale catchment covering a wide elevation range and experiencing multiple periods of intermittent snow cover annually. We expect the synthetic snow cover maps to provide added value through the high-resolution spatial information on snow appearance and disappearance, leading to better estimates of snow melt runoff. However, we also expect them to show some physical inconsistencies, particularly after periods of high snow accumulation. Should it prove promising, this approach can be used to study both past and future hydrological changes in any snow-fed catchment using ERA5 and CMIP6 climate data, potentially spanning the entire period 1950-2100. Additionally, this framework of synthetic satellite data generation could be expanded to other hydrological variables or to environmental image time series in other fields than hydrology.